Enhancing Reservoir Operations by Leveraging Large-Scale Data Sets for Multi-Objective Simulation and Optimization

The management of multipurpose reservoirs necessitates multiple and often conflicting objectives such as flood control, water supply, and downstream channel performance. This study introduces a novel and comprehensive framework that leverages large-scale data sets in conjunction with a multiobjective simulation-optimization (MOSO) model to optimize reservoir prerelease operations. By employing sophisticated optimization algorithms and multicriteria decision-making methods, this framework equips decision-makers with robust tools for optimizing operations under varying conditions. This study combines reservoir simulation models and flow routing techniques using available large-scale data sets to optimize prerelease operations based on flood forecasts in a novel way. The Nondominated Sorting Genetic Algorithm II is utilized to solve MOSO problems to generate Pareto-optimal solutions that elucidate the trade-offs between competing objectives. This framework is applied to the Green River watershed in Kentucky, integrating extensive data sets from diverse sources, and can be applied to other regions within the data set coverages. The findings indicate the applicability of the framework and the data sets in optimizing reservoir operations and highlight the framework’s effectiveness in significantly enhancing flood mitigation, improving water supply reliability, and optimizing downstream channel performance, surpassing traditional methods. Sensitivity analyses reveal the impact of varying initial storage levels and inflow conditions on system performance and highlight the necessity for adaptable, data-driven management strategies. This comprehensive, innovative, and robust framework offers improvements over traditional methods by addressing the complexities and data limitations inherent in managing multipurpose reservoir systems and paving the way for more sustainable and efficient water resource management.